Image Quality Assessment (IQA) is essential for the design and evaluation of imaging, display and image processing systems. While the goal of an IQA system is to mimic and quantitatively rate a Human Vision System (HVS), the complexity of such a vision system makes it difficult to define a synthetic algorithm able to provide consistent results across different distortion types and different image contents. Existing IQA methods can be broadly categorized into the following three groups: full-reference (FR) IQA methods, reduced-reference (RR) IQA methods, and no-reference (NR) IQA methods. The former two groups, i.e., the FR IQA and the RR IQA methods groups, take advantage of complete or partial information of the “perfect” reference image respectively, while the NR IQA methods are often designed to extract discriminative features or to calculate natural scene statistics to qualify the image quality. FR IQA methods can often achieve results comparable to HVS.
Since conventional IQA methods can provide consistent evaluations for different image contents when they rely on to one or more reference images as baselines, they can readily be used in supervised or semi-supervised conditions. In many cases, existing methods require that the reference image must be pixel-wise aligned with a distorted image for reliable assessment. Unfortunately, pixelwise aligned reference images are often unavailable or difficult to extract, and this largely limits deployment of IQA applications.